The machine learning community often measures the classification accuracy as a simple scalar performance metric for binary classification. Classification accuracy measures the ratio of correctly classified edits into rejected & accepted classes with respect to all classified edits. However, according to [1]}, accuracy might lead to an incorrect conclusion as the measure is highly sensitive to changes in data. In such cases, precision is a useful metric to capture the effect on a classifier performance of having a larger number of negative examples [2]}. In particular, precision measures the ratio of correctly classified edits into a class (i.e., rejected/accepted) with respect to all edits classified into that class. However, [1]} argued that precision is still sensitive to changes in the data distribution, and it cannot assert how many positive examples are classified incorrectly.
Unlike precision, recall is not sensitive to data distribution that measures the ratio of correctly classified edits with respect to the actually observed edits as true instances. However, any assessment based solely on recall would be inadequate, as it provides no insight into how many examples are incorrectly classified as positives.
Therefore, neither precision nor recall can provide a reliable assessment of classification performance [4]}. However, these individual scalar metrics can be combined to build more reliable classification performance measures. Specifically, these aggregated performance metrics include the F-measure that represents the harmonic mean of precision and recall. We thus measure precision, recall, F1-score, and overall accuracy to conclude the models' performance better. They can be measured as follows.
